In the field of wireless telecommunications networks, described below is a method to improve the channel estimate in broadband SIMO/MIMO cellular radio networks during abrupt interference variations (used acronyms are given at the end of the description). The method is suitable to, but it is not restricted, to be employed in the Base Station receivers for broadband multi-cell wireless systems based on frequency reuse in adjacent cells and, if needed, employing the SDMA technique in the same cell. The method could find particular application in cellular systems based on:                OFDM modulation and TDMA access at the physical layer, such as WiMAX and IEEE 802. 16-2004 Part 16: Air Interface for Fixed Broadband Wireless Access Systems for fixed Subscriber Stations.        OFDMA-TDMA access at the physical layer, such as WiMAX and IEEE 802.16e Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems for fixed and mobile Subscriber Stations.        ETSI TS 102 177 Broadband Radio Access Networks (BRAN); HIPERMAN; Physical (PHY) layer.        
In addition, modifications may be applied to receivers belonging to Subscriber Stations and/or point-to-point links.
The method described below may be applied to a multicell SIMO system with a single transmitting antenna and at least two receiving antennas. A first improvement is a SIMO system with NR>2 receiving antennas. A second improvement is a MIMO system with NT transmitting antennas and NR receiving antennas. The method estimates the channel response by only accounting for the NR signals received from the NR antennas, together with some a priori knowledge and some statistical assumptions on the noise and interference.
As known, the multipath fading together with co-channel interference from subscriber stations in the same or adjacent cells, are the major sources of SINR degradation at the output of the receivers. Usually the multi-cell interference is accounted by a covariance matrix (or noise power) that is assumed as constant, and applying a spatial filter on the received signals (and a pre-filter at the transmitter whenever available) to improve the SINR at the output.
Multiple antennas (SIMO/MIMO) is a known manner to obtain larger values of SINR. When designing the antenna array, diversity and beamforming are two different strategies typically adopted depending on the specific impairment, either interference or fading, that has to be contrasted. There are some degrees of freedom to be exploited when designing the antennas arrays and the receiver processing.                Diversity-oriented schemes take advantage of the spatial multiplex gain for reducing the fade margin. Large antenna spacing compared to the carrier wavelength is used, say larger than 5-8λ, so that signals are uncorrelated at the different antennas and can be processed by diversity-based algorithms, such as MRC. This algorithm needs the knowledge of channel responses at all antennas.        Beamforming-oriented schemes for interference rejection, using small spacing up to λ/2, so that signals are completely correlated at different antennas and beamforming techniques (e.g. MVDR) are adopted to filter out the interferences. Used algorithms require the knowledge of both channel response and the spatial features of the interference power.        
A method for estimating multiple OFDM channel response (as specified, for instance in IEEE 2004 or 802.16 e) for MIMO application, without explicit calculation of the DOAs, is described in EP 03425721 European patent application of the same Applicant, titled: METHOD FOR THE ESTIMATION AND TRACKING OF CHANNEL MODES IN MULTICARRIER COMMUNICATION SYSTEMS. Accordingly, the multiple channel response is modelled as a battery of NR×NT FIR filters packed up into a channel matrix, whose elements are all unknown and must be estimated in order to provide the receiver with an estimated channel response for detecting the transmitted data sequence. Initially the receiver performs the LS channel matrix estimate in correspondence of some training sequences mapped into a fixed number of OFDM subcarriers (pilots) and univocally associated to the transmitting antennas. The pilot subcarriers are opportunely distributed into preambles of the transmission frames planned (the preambles) at a rate depending on the variability of the channel: in case of fast variability training data could be sent also every OFDM symbol indeed. The channel estimate performed on the received training sequences avails of a copy of these sequences stored in the receiver. The physical parameters characterizing the channel, such as: cell dimensions, multipath delay/angle patterns, number and angular positions of the interferers, etc., are not made explicit in the channel matrix composition, nevertheless as the channel estimation is precise as the elements of the channel matrix implicitly reflect the effects of the physical parameters.
The unconstrained LS channel estimate is unavoidably noisy because of the cumbersome number of elements to be estimated, opposed to the limited length of the training sequences. The method of the cited document is aimed to reduce the dimension of the LS estimate to obtain a more precise estimation (lower MSE). The dimension of a generic matrix can be accounted by its rank, to say, the minimum number between independent columns or rows. Some algebraic handlings allow decomposing a generic matrix into more suitable equivalent canonical forms; the eigenvalues-eigenvectors decomposition of the LS channel matrix is used. In the real propagation scenario, some known rank reduction methods, such as MDL, starting from the initial full-rank dimension adaptively selects only the most significant leading eigenvectors disregarding the others. The LS-estimated channel matrix is multiplied by a weight matrix to de-correlate, both spatially and temporally, the relevant interferences, at first. Decorrelation is also termed “whitening”, for analogy with the white noise completely temporally uncorrelated (flat frequency band), the weighting matrix (matrices) is called “whitening filter”, consequently. The whitened channel matrix is submitted to a modal filtering operating on doubly-spatial temporal domain. Modal filtering includes both modal analysis and modal synthesis. Modal analysis allows extracting the only spatial-temporal information actually effective to the estimate; it includes: spatial mode identification, temporal mode identification, and modal components estimation. Modal synthesis gets back a whitened channel matrix with lower rank. The original noise and interference correlations are then restored by inversely weighting (de-whitening) the modal filtered matrix; this operation doesn't change the reduced rank.
Outlined Technical Problem
The way to manage the interference is a critical issue when it cannot be assumed as stationary. In practice, the beneficial effects on greater SINR values obtainable by multiple antennas are worsened by variations of the interfering power induced by:                1. Uncoordinated (asynchronous) accesses of the users among different cells of the multicell environment; for example, new terminals might become active in any of the interfering cells thus generating an abrupt power increase of the interfering signals during the transmission. Dually, active terminals might switch off, thus generating an abrupt power decrease of the interfering signals during the transmission.        2. Vehicular motion of the terminal stations which changes the characteristics of the channel.        
Since the interfering power is subject to large fluctuations, in correspondence the SINR deviations may be remarkable (e.g., for a log-normal shadowing having a standard deviation equal to 8 dB, SINR level changes up to 15-20 dB are likely to happen).
The channel estimation and tracking method described in the aforementioned EP 03425721 only tracks the time varying space-time channel for the user of interest under the a priori assumption of stationary interference with constant power. The tracking method is therefore completely unable to remedy for abrupt interference power changes. As a consequence when, in spite of the quasi-constant interference assumption, the interference power suddenly changes for the underlined causes, the updating of the interference covariance matrix (based on a running average with forgetting factor) tends to mask the sudden variation. According to the above, the known method is completely unable to adapt the interference estimate to the real situation. As the interference covariance matrix is used to whiten the channel estimate before submitting it to a modal filtering for the rank reduction, the imprecise estimate of the interference unavoidably reflects into inaccuracy of the final channel estimate. In this event the receiver might incorrectly detect the transmitted data sequence and the BER at the output of the receiver increases. For BER values greater than the maximum permissible threshold, the communication in progress on the interfered channel is lost and the performances of the system are worsened consequently.